Overview

Dataset statistics

Number of variables12
Number of observations2824
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory286.8 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_itens is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with u_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
u_basket_size is highly overall correlated with qtde_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 42.49949808)Skewed
frequency is highly skewed (γ1 = 22.86486892)Skewed
qtde_returns is highly skewed (γ1 = 21.82144361)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.2%) zerosZeros
avg_recency_days has 51 (1.8%) zerosZeros
qtde_returns has 1524 (54.0%) zerosZeros

Reproduction

Analysis started2023-11-15 15:53:16.969602
Analysis finished2023-11-15 15:54:16.631061
Duration59.66 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2824
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15299.481
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:17.161674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.15
Q113826.25
median15270.5
Q316801.75
95-th percentile17953.55
Maximum18287
Range5940
Interquartile range (IQR)2975.5

Descriptive statistics

Standard deviation1714.421
Coefficient of variation (CV)0.11205747
Kurtosis-1.2049882
Mean15299.481
Median Absolute Deviation (MAD)1484
Skewness0.0027127875
Sum43205733
Variance2939239.5
MonotonicityNot monotonic
2023-11-15T12:54:17.668964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
14655 1
 
< 0.1%
17029 1
 
< 0.1%
13220 1
 
< 0.1%
15985 1
 
< 0.1%
15172 1
 
< 0.1%
13692 1
 
< 0.1%
15745 1
 
< 0.1%
16639 1
 
< 0.1%
15491 1
 
< 0.1%
Other values (2814) 2814
99.6%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18265 1
< 0.1%
18263 1
< 0.1%
18261 1
< 0.1%
18260 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2808
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2820.8528
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:18.314144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile251.876
Q1614.015
median1142.935
Q32388.8375
95-th percentile7433.5075
Maximum279138.02
Range279101.46
Interquartile range (IQR)1774.8225

Descriptive statistics

Standard deviation10401.624
Coefficient of variation (CV)3.687404
Kurtosis375.53948
Mean2820.8528
Median Absolute Deviation (MAD)683.02
Skewness17.129036
Sum7966088.4
Variance1.0819378 × 108
MonotonicityNot monotonic
2023-11-15T12:54:18.750070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1353.74 2
 
0.1%
734.94 2
 
0.1%
2092.32 2
 
0.1%
1078.96 2
 
0.1%
178.96 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
1025.44 2
 
0.1%
889.93 2
 
0.1%
598.2 2
 
0.1%
Other values (2798) 2804
99.3%
ValueCountFrequency (%)
36.56 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
77.4 1
< 0.1%
84.65 1
< 0.1%
90.3 1
< 0.1%
93.35 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency_days
Real number (ℝ)

ZEROS 

Distinct257
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.105524
Minimum0
Maximum373
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:19.203111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q374
95-th percentile215
Maximum373
Range373
Interquartile range (IQR)64

Descriptive statistics

Standard deviation70.215004
Coefficient of variation (CV)1.208405
Kurtosis3.2614675
Mean58.105524
Median Absolute Deviation (MAD)24
Skewness1.8722169
Sum164090
Variance4930.1468
MonotonicityNot monotonic
2023-11-15T12:54:19.731019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.5%
4 87
 
3.1%
2 86
 
3.0%
3 85
 
3.0%
8 76
 
2.7%
10 69
 
2.4%
9 66
 
2.3%
7 65
 
2.3%
17 62
 
2.2%
22 56
 
2.0%
Other values (247) 2073
73.4%
ValueCountFrequency (%)
0 33
 
1.2%
1 99
3.5%
2 86
3.0%
3 85
3.0%
4 87
3.1%
5 43
1.5%
7 65
2.3%
8 76
2.7%
9 66
2.3%
10 69
2.4%
ValueCountFrequency (%)
373 1
 
< 0.1%
372 1
 
< 0.1%
369 1
 
< 0.1%
366 1
 
< 0.1%
360 1
 
< 0.1%
358 3
0.1%
354 1
 
< 0.1%
337 1
 
< 0.1%
336 2
0.1%
334 1
 
< 0.1%

qtde_invoices
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.983711
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:20.279060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.0058675
Coefficient of variation (CV)1.5050639
Kurtosis186.37707
Mean5.983711
Median Absolute Deviation (MAD)2
Skewness10.688153
Sum16898
Variance81.10565
MonotonicityNot monotonic
2023-11-15T12:54:20.863102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 825
29.2%
3 503
17.8%
4 394
14.0%
5 237
 
8.4%
6 173
 
6.1%
7 138
 
4.9%
8 98
 
3.5%
9 69
 
2.4%
10 55
 
1.9%
11 54
 
1.9%
Other values (45) 278
 
9.8%
ValueCountFrequency (%)
2 825
29.2%
3 503
17.8%
4 394
14.0%
5 237
 
8.4%
6 173
 
6.1%
7 138
 
4.9%
8 98
 
3.5%
9 69
 
2.4%
10 55
 
1.9%
11 54
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_itens
Real number (ℝ)

HIGH CORRELATION 

Distinct1653
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1648.7755
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:21.347385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile114.15
Q1323
median683.5
Q31468.25
95-th percentile4538.4
Maximum196844
Range196842
Interquartile range (IQR)1145.25

Descriptive statistics

Standard deviation5840.5383
Coefficient of variation (CV)3.5423491
Kurtosis493.5989
Mean1648.7755
Median Absolute Deviation (MAD)441.5
Skewness18.32383
Sum4656142
Variance34111888
MonotonicityNot monotonic
2023-11-15T12:54:21.881153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 8
 
0.3%
246 8
 
0.3%
219 7
 
0.2%
1200 7
 
0.2%
200 7
 
0.2%
493 7
 
0.2%
300 7
 
0.2%
272 7
 
0.2%
394 7
 
0.2%
Other values (1643) 2748
97.3%
ValueCountFrequency (%)
2 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
27 2
0.1%
30 1
< 0.1%
32 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%
50255 1
< 0.1%

qtde_products
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.23477
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:22.503697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median71
Q3142
95-th percentile393.7
Maximum7838
Range7836
Interquartile range (IQR)108

Descriptive statistics

Standard deviation275.58898
Coefficient of variation (CV)2.1490971
Kurtosis341.89962
Mean128.23477
Median Absolute Deviation (MAD)44
Skewness15.455648
Sum362135
Variance75949.287
MonotonicityNot monotonic
2023-11-15T12:54:23.142803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 39
 
1.4%
35 36
 
1.3%
27 31
 
1.1%
26 31
 
1.1%
29 30
 
1.1%
31 28
 
1.0%
25 28
 
1.0%
15 28
 
1.0%
19 27
 
1.0%
33 26
 
0.9%
Other values (457) 2520
89.2%
ValueCountFrequency (%)
2 11
0.4%
3 13
0.5%
4 17
0.6%
5 16
0.6%
6 26
0.9%
7 15
0.5%
8 14
0.5%
9 20
0.7%
10 19
0.7%
11 23
0.8%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1944
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.976045
Minimum2.15
Maximum13305.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:23.662836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile4.7615
Q112.21
median17.89
Q324.8825
95-th percentile87.165
Maximum13305.5
Range13303.35
Interquartile range (IQR)12.6725

Descriptive statistics

Standard deviation272.78159
Coefficient of variation (CV)7.3772518
Kurtosis2009.4589
Mean36.976045
Median Absolute Deviation (MAD)6.4
Skewness42.499498
Sum104420.35
Variance74409.797
MonotonicityNot monotonic
2023-11-15T12:54:24.202874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.49 7
 
0.2%
17.66 6
 
0.2%
19.06 6
 
0.2%
16.53 6
 
0.2%
16.82 6
 
0.2%
17.13 5
 
0.2%
17.71 5
 
0.2%
10 5
 
0.2%
16.92 5
 
0.2%
19.44 5
 
0.2%
Other values (1934) 2768
98.0%
ValueCountFrequency (%)
2.15 1
< 0.1%
2.43 1
< 0.1%
2.46 1
< 0.1%
2.51 1
< 0.1%
2.52 1
< 0.1%
2.65 1
< 0.1%
2.66 1
< 0.1%
2.71 1
< 0.1%
2.76 1
< 0.1%
2.77 1
< 0.1%
ValueCountFrequency (%)
13305.5 1
< 0.1%
4453.43 1
< 0.1%
1687.2 1
< 0.1%
1377.08 1
< 0.1%
952.99 1
< 0.1%
872.13 1
< 0.1%
841.02 1
< 0.1%
651.17 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1218
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.078754
Minimum0
Maximum366
Zeros51
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:24.832747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.55
Q130
median54
Q392.666667
95-th percentile212.7
Maximum366
Range366
Interquartile range (IQR)62.666667

Descriptive statistics

Standard deviation65.524224
Coefficient of variation (CV)0.89662482
Kurtosis4.1469104
Mean73.078754
Median Absolute Deviation (MAD)28.703297
Skewness1.9133911
Sum206374.4
Variance4293.424
MonotonicityNot monotonic
2023-11-15T12:54:25.508251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51
 
1.8%
70 20
 
0.7%
31 20
 
0.7%
21 16
 
0.6%
14 16
 
0.6%
42 15
 
0.5%
55 15
 
0.5%
46 15
 
0.5%
49 14
 
0.5%
56 13
 
0.5%
Other values (1208) 2629
93.1%
ValueCountFrequency (%)
0 51
1.8%
0.0303030303 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3333333333 1
 
< 0.1%
0.8571428571 1
 
< 0.1%
1 8
 
0.3%
1.5 1
 
< 0.1%
1.819512195 1
 
< 0.1%
1.878787879 1
 
< 0.1%
2 3
 
0.1%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
363 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1226
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087058954
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:26.170916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088164204
Q10.015873016
median0.024793388
Q30.043478261
95-th percentile0.14937847
Maximum17
Range16.99455
Interquartile range (IQR)0.027605245

Descriptive statistics

Standard deviation0.43634384
Coefficient of variation (CV)5.0120501
Kurtosis810.31552
Mean0.087058954
Median Absolute Deviation (MAD)0.011000285
Skewness22.864869
Sum245.85449
Variance0.19039595
MonotonicityNot monotonic
2023-11-15T12:54:26.743526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 47
 
1.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.6%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.02941176471 14
 
0.5%
0.03448275862 14
 
0.5%
0.02564102564 13
 
0.5%
0.02127659574 13
 
0.5%
Other values (1216) 2642
93.6%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
0.2%
2 47
1.7%
1.142857143 1
 
< 0.1%
1 8
 
0.3%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%

qtde_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct204
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.408994
Minimum0
Maximum9014
Zeros1524
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:27.283610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile93.7
Maximum9014
Range9014
Interquartile range (IQR)8

Descriptive statistics

Standard deviation288.11229
Coefficient of variation (CV)8.373168
Kurtosis582.17626
Mean34.408994
Median Absolute Deviation (MAD)0
Skewness21.821444
Sum97171
Variance83008.692
MonotonicityNot monotonic
2023-11-15T12:54:27.800649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1524
54.0%
1 131
 
4.6%
2 118
 
4.2%
3 82
 
2.9%
4 72
 
2.5%
6 63
 
2.2%
5 56
 
2.0%
12 46
 
1.6%
8 39
 
1.4%
9 38
 
1.3%
Other values (194) 655
23.2%
ValueCountFrequency (%)
0 1524
54.0%
1 131
 
4.6%
2 118
 
4.2%
3 82
 
2.9%
4 72
 
2.5%
5 56
 
2.0%
6 63
 
2.2%
7 38
 
1.3%
8 39
 
1.4%
9 38
 
1.3%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1949
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.44568
Minimum1
Maximum6009.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:28.408696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.033
Q1102
median171.04
Q3277
95-th percentile584.275
Maximum6009.33
Range6008.33
Interquartile range (IQR)175

Descriptive statistics

Standard deviation261.81746
Coefficient of variation (CV)1.1361353
Kurtosis113.6886
Mean230.44568
Median Absolute Deviation (MAD)81.375
Skewness7.6467076
Sum650778.6
Variance68548.385
MonotonicityNot monotonic
2023-11-15T12:54:29.069897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
86 9
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
197 7
 
0.2%
136 7
 
0.2%
105 7
 
0.2%
208 7
 
0.2%
82 7
 
0.2%
73 7
 
0.2%
Other values (1939) 2746
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
3.33 1
< 0.1%
5.33 1
< 0.1%
5.67 1
< 0.1%
6.14 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
11.88 1
< 0.1%
ValueCountFrequency (%)
6009.33 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2733.94 1
< 0.1%
2518.77 1
< 0.1%
2160.33 1
< 0.1%
2082.23 1
< 0.1%
2000 1
< 0.1%
1903.5 1
< 0.1%
1866.93 1
< 0.1%

u_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct982
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.109285
Minimum1
Maximum299.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-15T12:54:29.723177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4745
Q110.1125
median17.25
Q328.0275
95-th percentile56.6655
Maximum299.71
Range298.71
Interquartile range (IQR)17.915

Descriptive statistics

Standard deviation18.850429
Coefficient of variation (CV)0.85260237
Kurtosis23.865882
Mean22.109285
Median Absolute Deviation (MAD)8.25
Skewness3.1336106
Sum62436.62
Variance355.33866
MonotonicityNot monotonic
2023-11-15T12:54:30.427392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 45
 
1.6%
11 31
 
1.1%
14 31
 
1.1%
9 27
 
1.0%
1 27
 
1.0%
7.5 27
 
1.0%
10.5 26
 
0.9%
17.5 26
 
0.9%
17 24
 
0.8%
9.5 24
 
0.8%
Other values (972) 2536
89.8%
ValueCountFrequency (%)
1 27
1.0%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.33 2
 
0.1%
1.5 7
 
0.2%
1.57 2
 
0.1%
1.67 4
 
0.1%
1.83 1
 
< 0.1%
2 22
0.8%
2.05 1
 
< 0.1%
ValueCountFrequency (%)
299.71 1
< 0.1%
203.5 1
< 0.1%
145 1
< 0.1%
136.12 1
< 0.1%
135.5 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%
110.33 1
< 0.1%
110 1
< 0.1%

Interactions

2023-11-15T12:54:09.948375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:17.629806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:20.804932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:26.261580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:31.100743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:36.310429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:40.496527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:45.280390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:50.336625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:55.912814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:00.240730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:04.889859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:10.418407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:17.823359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:21.122882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:26.676069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:31.406209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:36.612271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:40.881086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:45.624625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:50.781670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:56.371969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:00.487639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:05.183631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:11.400067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:18.011337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:21.526970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:27.137388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:31.783437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:36.945414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:41.279376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:46.043263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:51.257107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:56.685478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:00.720953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:05.477563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:11.847258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:18.236146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:22.260591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:27.603851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:32.140900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:37.377735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:41.711046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:46.451101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:51.675126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:57.014745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:01.077002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:05.752212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:12.288552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:18.520306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:22.729658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:28.032319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:32.544067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:37.841890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:42.068673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:46.931825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:52.193655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:57.295419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:01.485606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:06.247939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:12.804270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:18.742775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:23.160441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:28.512122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:33.054003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:38.178834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:42.461494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:47.409448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:52.728861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:57.725932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:01.917180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:06.752590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:13.314944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:18.960780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:23.648287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:28.959399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:33.572419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:38.546389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:42.750415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:47.710493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:53.123819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:58.084287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:02.411449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:07.241817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:13.650700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:19.228484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:24.144871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:29.347808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:33.970910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:38.883446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:43.081328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:48.033042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:53.516927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:58.411379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:02.848136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:07.698485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:14.046184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:19.578825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:24.612819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:29.729701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:34.406520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:39.276046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:43.475759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:48.366869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:54.006079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:58.785535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:03.292202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:08.200885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:14.419946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:19.895849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:25.086749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:30.059322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:34.907620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:39.579390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:43.989599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:48.682911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:54.512497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:59.237005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:03.734489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:08.681564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:14.732399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:20.202164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:25.394000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:30.337234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:35.647431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:39.860876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:44.441087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:49.061318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:54.949228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:59.549042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:04.163748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:09.079645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:15.022798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:20.509353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:25.793007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:30.697716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:35.996994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:40.215568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:44.870798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:49.544828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:55.446690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:53:59.915265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:04.599940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-15T12:54:09.532721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-15T12:54:30.978663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
customer_id1.000-0.0940.0140.004-0.0850.008-0.146-0.0320.027-0.062-0.1230.007
gross_revenue-0.0941.000-0.3790.7640.9200.7200.283-0.3430.2140.4630.6040.280
recency_days0.014-0.3791.000-0.453-0.372-0.3980.0320.188-0.097-0.189-0.111-0.111
qtde_invoices0.0040.764-0.4531.0000.7050.6590.099-0.4530.2720.4310.1340.018
qtde_itens-0.0850.920-0.3720.7051.0000.7080.202-0.3180.1990.4250.7630.312
qtde_products0.0080.720-0.3980.6590.7081.000-0.374-0.2860.1690.3280.4070.723
avg_ticket-0.1460.2830.0320.0990.202-0.3741.000-0.0690.0630.1960.202-0.623
avg_recency_days-0.032-0.3430.188-0.453-0.318-0.286-0.0691.000-0.971-0.216-0.0270.062
frequency0.0270.214-0.0970.2720.1990.1690.063-0.9711.0000.1520.008-0.072
qtde_returns-0.0620.463-0.1890.4310.4250.3280.196-0.2160.1521.0000.2120.022
basket_size-0.1230.604-0.1110.1340.7630.4070.202-0.0270.0080.2121.0000.431
u_basket_size0.0070.280-0.1110.0180.3120.723-0.6230.062-0.0720.0220.4311.000

Missing values

2023-11-15T12:54:15.528395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-15T12:54:16.144490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
0178505391.21372.034.01733.0297.018.150.03030317.00000040.050.978.74
1130473232.5956.09.01390.0171.018.9039.6250000.02830235.0154.4419.00
2125836705.382.015.05028.0232.028.9026.5000000.04032350.0335.2015.47
313748948.2595.05.0439.028.033.8769.5000000.0179210.087.805.60
415100876.00333.03.080.03.0292.0020.0000000.07317122.026.671.00
5152914623.3025.014.02102.0102.045.3326.7692310.04011529.0150.147.29
6146885630.877.021.03621.0327.017.2218.3000000.057221399.0172.4315.57
7178095411.9116.012.02057.061.088.7232.4545450.03352041.0171.425.08
81531160767.900.091.038194.02379.025.544.1444440.243316474.0419.7126.14
9160982005.6387.07.0613.067.029.9347.6666670.0243900.087.579.57
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
563717468137.0010.02.0116.05.027.404.0000000.4000000.058.002.5
564813596697.045.02.0406.0166.04.207.0000000.2500000.0203.0083.0
5654148931237.859.02.0799.073.016.962.0000000.6666670.0399.5036.5
565617852114.3411.02.053.024.04.760.0000002.0000000.026.5012.0
567317772182.7710.02.058.053.03.450.0000002.0000000.029.0026.5
567914126706.137.03.0508.015.047.081.5000000.75000050.0169.335.0
568016479300.8310.02.0102.035.08.600.0000002.0000000.051.0017.5
5685135211092.391.03.0733.0435.02.514.5000000.3000000.0244.33145.0
569515060301.848.04.0262.0120.02.520.3333332.0000000.065.5030.0
57651600012393.702.03.05110.09.01377.080.0000003.0000000.01703.333.0